Data Visualization Techniques

Venustiano Soancatl Aguilar

Definitions

Scientific Visualization

“The use of computers or techniques for comprehending data or to extract knowledge from the results of simulations, computations, or measurements”.
Examples: CT Scans, MRI, Inertial sensors.
[McCormick et al., 1987]

Information Visualization

“Visualization applied to abstract quantities and relations in order to get insight in the data”.
Examples: matplotlib, seaborn, ggplot2
[Chi, 2000]

Visual Analytics

“The science of analytical reasoning facilitated by interactive visual interfaces”.
Examples: Tableau, Plotly, Bokeh, Panel, etc.
[Thomas and Cook, 2005]

When is visualization useful?

  1. Too much data:
    • do not have time to analyze it all (or read the analysis results)
    • show an overview, discover which questions are relevant
    • refine search either visually or analytically
  2. Qualitative / complex questions:
    • cannot capture the question compactly/exactly in a query
    • question/goal is inherently qualitative: understand what is going on
    • show an overview, answer the question by seeing relevant patterns
  3. Communication:
    • transfer results to different (non-technical) stakeholders
    • learn about a new domain or problem

Why is Visualization Useful?

  • (in)validate the fit of a given model with a dataset
  • find the distribution of values over a given domain
  • find the correlation (or lack thereof) of several variables
  • answer precise (quantitative) questions

Anscombe’s quartet — original datasets described by F. J. Anscombe (1973). Image file: Wikimedia Commons: Anscombe.svg. See the Wikipedia article “Anscombe’s quartet” for background and references. Accessed 2025-10-26.

Why is Visualization?

  • find support for a new model in the data
  • find which model best fits a dataset
  • find the phenomenon behind the data
  • answer more vague (qualitative) questions